Transcript Large-Scale MIMO in Cellular Networks: Hardware Challenges

Large-Scale MIMO in
Cellular Networks
Hardware Challenges and High Energy Efficiency
Emil Björnson‡*
Joint work with: Jakob Hoydis†,
Marios Kountouris‡, and Mérouane Debbah‡
‡Alcatel-Lucent
Chair on Flexible Radio and Department of
Telecommunications, Supélec, France
*Signal
Processing Lab, KTH Royal Institute of Technology, Sweden
†Bell
2013-09-26
Laboratories, Alcatel-Lucent, Stuttgart, Germany
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
1
Outline
• Introduction
- Need for improved spectral efficiency
- How to improve it?
- Large-scale multiple-input multiple-output (MIMO) systems
• System Model with Hardware Impairments
- Non-linearities, phase noise, etc.
- How can it affect the system performance?
• New Problems & New Results
- Channel Estimation, Capacity Bounds, and Energy Efficiency
- Some properties are changed by impairments, some are not
• Conclusions & Outlook
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
2
Introduction
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
3
Challenge of Network Traffic Growth
• Data Dominant Era
- 66% annual traffic growth
- Exponential increase!
• Is this Growth Sustainable?
- User demand will increase
- Growth = Increase in supply
- Increased traffic supply only if
network revenue is sustained!
Source: Cisco Visual Networking Index
• Is There a Need for Magic?
- No! Conventional network evolution
- What will be the next step?
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
4
What are the Next Steps?
• More Frequency Spectrum
- Scarcity in conventional bands: Use mmWave, cognitive radio
- Joint optimization of current networks (Wifi, 2G/3G/4G)
Our Focus:
• Improved Spectral Efficiency
- More antennas/km2 (space division multiple access)
• What Limits the Spectral Efficiency?
2013-09-26
Propagation losses and transmit power
Inter-user interference
Limited channel knowledge
Channel capacity
Signal processing complexity
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
5
New Paradigm: Large Antenna Arrays
• New Remarkable Network Architecture
-
MIMO: Multi-antenna base stations and many users
Use large arrays at base stations: #antennas ≫ #users ≫ 1
Principle: Many degrees of freedom in space
Narrow beamforming
2013 IEEE Marconi Prize Paper Award:
Thomas Marzetta, “Noncooperative Cellular
Wireless with Unlimited Numbers of Base
Station Antennas," IEEE Transactions on
Wireless Communications, 2010.
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
6
New Paradigm: Large Antenna Arrays (2)
• Everything Seems to Become Better [1]
-
Large array gain (improves channel conditions)
Higher capacity (more antennas  more users)
Orthogonal channels (little inter-user interference)
Robustness to imperfect channel knowledge
Linear processing near-optimal (low complexity)
[1] F. Rusek, D. Persson, B. Lau, E. Larsson, T. Marzetta, O. Edfors,
F. Tufvesson, “Scaling up MIMO: Opportunities and challenges with
very large arrays,” IEEE Signal Process. Mag., 2013.
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
7
Where are the Gains Coming From?
• Time-reversal processing = Matched filtering!
-
Example: 𝑁 antennas
𝑁
Two user channels: 𝐡1𝐻 , 𝐡𝐻
2 ∈ℂ
Zero-mean i.i.d. entries
Unit variance
𝐡1𝐻
𝐡𝐻
2
- Matched filtering: 𝐰1 = 𝐡1
- Strong signal gain:
- Interference vanish:
𝟏 𝐻
𝐡 𝐰
𝑵 1 1
𝟏 𝐻
𝐡 𝐰
𝑵 2 1
𝟏
𝑵
𝟏
→ E[𝐡𝐻
2 𝐰1 ]
𝑵
= | 𝐡1 |2 → 1 as 𝑁 → ∞
= 0 as 𝑁 → ∞
• What vanishes?
- Everything not matched to the channel:
Inter-user interference, leakage from imperfect 𝐰1 , noise, etc.
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
8
Analytical and Practical Weaknesses
• Main Properties Proved by Asymptotic Analysis
- Are conventional models applicable?
• Simplified Channel Modeling
- Conventional model breaks down as 𝑁 → ∞
- One can receive more power than transmitted!
- Prototypes and measurements partially confirm the results:
Interference almost vanishes
• Are there any Hardware Limitations?
- Low-cost equipment desirable for large arrays
- Theoretical treatment of hardware impairments is missing!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
9
Transceiver Hardware Impairments
• Physical Hardware is Non-Ideal
- Oscillator phase noise, amplifier non-linearities,
IQ imbalance in mixers, etc.
- Can be mitigated, but residual errors remain!
• Impact of Residual Hardware Impairments
- Mismatch between the intended and emitted signal
- Distortion of received signal
- Limits spectral efficiency in high-power regime [2]
What happens in large-𝑵 regime?
Will everything still get better?
[2]: E. Björnson, P. Zetterberg, M. Bengtsson, B. Ottersten,
“Capacity Limits and Multiplexing Gains of MIMO Channels with
Transceiver Impairments,” IEEE Communications Letters, 2013
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
10
System Model with
Hardware Impairments
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
11
Our Focus: Point-to-Point Channel
• Scenario
-
Base station (BS): 𝑁 antennas
User terminal (UT): 1 antenna
Channel vector
Rayleigh fading:
• Properties of Covariance Matrix 𝐑
- Bounded spectral norm as 𝑁 grows
- Due to law of energy conservation
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
12
Our Focus: Point-to-Point Channel (2)
• Time-Division Duplex (TDD)
- Uplink estimation overhead does not scale with 𝑁
- Exploit channel reciprocity
Downlink beamforming:
Uplink reception
using 𝐡
User only needs
to estimate h𝐻 w
Estimation
of h
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
13
How do Model Hardware Impairments?
• Exact Characterization is Very Complicated
- Many different types of impairments
- Many different algorithms to mitigate them
- Only the combined impact is needed!
• Good and Simple Model of Residual Distortion
- Additive distortion noise
- From measurements: Variance scales with signal power
Gaussian distribution
[3]: T. Schenk, “RF Imperfections in High-Rate Wireless Systems:
Impact and Digital Compensation”. Springer, 2008
[4]: M. Wenk, “MIMO-OFDM Testbed: Challenges, Implementations,
and Measurement Results”. Hartung-Gorre, 2010
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
14
Generalized System Model: Downlink
• Conventional Model:
• Generalized Model with Impairments:
- Distortion per antenna: Prop. to transmitted/received power
Proportionality constants
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
15
Generalized System Model: Uplink
• Conventional Model:
• Generalized Model with Impairments:
- Distortion per antenna: Prop. to transmitted/received power
Proportionality constants
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
16
Interpretation of Distortion Model
• Gaussian Distortion Noise
- Independent between antennas
- Depends on beamforming
- Still uncorrelated directivity
• Error Vector Magnitude (EVM)
- Quality of transceivers:
- LTE requirements: 0 ≤ EVM ≤ 0.17 (smaller  higher rates)
- Distortion will not vanish at high SNR!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
17
New Problems & New Results
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
18
Result 1: Channel Estimation
• Channel Estimation from Pilot Transmission
- Send known signal to observe the channel
• Problem: Conventional Estimators Cannot be Used
- Relies on channel observation in independent noise
- Distortion noise is correlated with the channel
• Contribution: New Linear MMSE Estimator
- Handles distortions that are correlated with channel
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
19
Result 1: Channel Estimation (2)
• MSE in i.i.d. case
New Insights
𝑁 = 50, 𝐒 = 𝐈, 𝐑 = correlation 0.7
Low SNR: Small difference
High SNR: Error floor
Error floor in i.i.d. case:
Very different MSE but no
need to change estimator
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
20
Result 2: Capacity Behavior
• Question: How is Throughput Affected?
- Conventionally: Capacity → ∞ with #antennas or power
• Contribution: New Characterization of UL/DL Capacities
- Upper bound: Channels are known, no interference
- Lower bound: Matched filtering, new LMMSE estimator, treat
interference/channel uncertainty as noise
• Asymptotic Upper Limits:
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
21
Result 2: Capacity Behavior (2)
• Bounded Capacity
- Small impact of
BS impairments
- Other spatial
signature!
New Insights
SNR = 20 dB, 𝐑 = 𝐒 = 𝐈
Capacity limited by UT hardware
𝑁 → ∞: No impact of BS!
Major gains for 𝑁 up to 50−100
Minor gains above 𝑁 = 100
Upper/lower limits almost same
Very different from ideal case!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
22
Result 3: Energy Efficiency
• Energy Efficiency in bits/Joule
Capacity [bits/channel use]
Power [Joule/channel use]
- Capacity limited as 𝑁 → ∞
- EE =
New Insights
Theorem
Reduce power as
1
,
𝑁𝑡
𝑡<
1
2
Non-zero capacity as 𝑁 → ∞
SNR = 20 dB at N=1 , 𝐑 = 𝐒 = 𝐈
Power reduction from array gain
Same as with ideal hardware!
Capacity lower bounded by
EE grows without bound!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
23
Result 3: Energy Efficiency (2)
• Does an Infinite EE Make Sense?
- No! We only consider transmitted power, no circuit power
Capacity
- EErefined =
Transmit power + N ∙ Antenna power+ Static Circuit Power
New Insights
EE maximized at finite 𝑁
Depends on the circuit power
that scales with 𝑁
Large-arrays become more
feasible with time!
Impairments has minor impact!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
24
Result 4: Impact on Cellular Networks
• Question: Impact of Hardware Impairments on a Network?
- Is there any fundamental difference?
• Observation: Distortion Noise = Self-interference
- Self-interference is 20-30 dB weaker than signal
- Inter-user interference is negligible if weaker than this!
- Uncorrelated interference always vanish as 𝑁 → ∞!
• Important Special Case: Pilot Contamination
- Necessary to reuse pilot signals across cells
- Estimate
is correlated with interfering pilot signals
- Corresponding interference will not vanish as 𝑁 → ∞!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
25
Result 4: Impact on Cellular Networks (2)
• Contribution: Simple Inter-Cell Coordination Principle
- Same pilot to users causing weak interference to each other:
Interference drowns in distortions
- Other stronger interference: Vanishes as 𝑁 → ∞
New Insights
Pilot contamination is negligible
if weaker than distortion
This condition can be fulfilled
by pilot allocation!
Other interference vanishes
asymptotically, as usual
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
26
Conclusions & Outlook
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
27
Conclusions
• New Paradigm: Large Antenna Arrays at BSs
- Promise high asymptotic spectral and energy efficiency
- Matched filtering is asymptotically optimal
• Physical Hardware has Impairments
-
2013-09-26
Creates distortion noise: Limits signal quality
Limits estimation and prevents extraordinary capacity
High energy efficiency is still possible!
Pilot contamination becomes a smaller issue
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
28
Outlook
• Is Matched Filtering Good also at Finite 𝑁?
- Depends on SNR, user scheduling, etc.
- Optimal solution: Rotate matched filter to reduce interference
- Examples: MMSE beamforming, regularized zero-forcing
• No Impact of Hardware Impairments at BSs as 𝑁 → ∞
- Hardware can be degraded with array size
- κ-parameters can be scaled as 𝑁
- Important property for practical deployments!
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
29
Thank You for Listening!
Questions?
Main Reference:
E. Björnson, J. Hoydis, M. Kountouris, M. Debbah,
“Massive MIMO Systems with Non-Ideal Hardware:
Energy Efficiency, Estimation, and Capacity Limits,”
Submitted to IEEE Trans. Information Theory, arXiv:1307.2584
All Papers Available:
http://flexible-radio.com/emil-bjornson
2013-09-26
Large-Scale MIMO in Cellular Networks, Emil Björnson (Supélec and KTH)
30